Space applications
training courses

Master 2 Mathématiques et Intelligence artificielle

• Organisations :
  • Faculté des Sciences d'Orsay
    (Université Paris-Saclay)
  • CentraleSupélec
    (Université Paris-Saclay)
• Way of training : Initial education
• Language(s): French
• Place: Campus d'Orsay, CentraleSupélec, Campus Palaiseau (ENSAE) (French Regions: Ile-de-France)
• Prepared diploma/grade/title: Master or DNM: French National Master Degree
• 1st required level : French baccalauréat + 3
• This training is included in RNCP / RS

Course Details

Objectives : The M2 Mathematics and Artificial Intelligence is designed as a continuation of the M1 Mathematics and Artificial Intelligence.

It is run jointly by the Mathematics department of Orsay and Centrale-Supelec, with the support of the computer science department and the SaclAI-school program.

Mathematics plays an important role in artificial intelligence (AI), and in particular in learning. Data science, which combines mathematical modelling, statistics, computer science, visualization and applications, aims to move from the storage and dissemination of information to the creation of knowledge.

This transition from data to knowledge requires an interdisciplinary approach that relies heavily on the statistical processing of information (mathematical statistics, numerical statistics, statistical learning or machine learning).

The large dimension leads to the use of new tools from different branches of mathematics (functional analysis, numerical analysis, convex and non-convex optimization) which must be understood.

This course allows you to master the mathematical issues and techniques that are the basis of machine learning, while giving solid computer skills for the development of projects in learning, data science and AI.

Degree Level (EU) : 7 - (EQC level or equivalent)

Dedicated web site : https://www.imo.universite-paris-saclay.fr/fr/etudiants/masters/mathematiques-et-applications/m2/m2-mathematique-et-intelligence-artificielle/

Space Technologies and Scientific Disciplines

• Data processing and analysis (Signal and image processing, big data, learning algorithms, artificial intelligence...)